Orbital Materials

weights

open(Apache-2.0 ungated checkpoints loaded through the orb_models.forcefield.pretrained module)

data

open(MPTrj, Alexandria, OMat24, OMol25, and OPoly26 training datasets publicly released)

code

partial(inference, simulation wrappers, and a finetuning script

license

Apache-2.0(OSI)

Orb names a released public dataset behind every checkpoint and licenses the models under Apache-2.0, but the repository ships adaptation code rather than a pretraining run, which keeps it from reaching a fully open, from-scratch release.

• https://raw.githubusercontent.com/orbital-materials/orb-models/main/LICENSE recorded 2026-10-13

  Apache License 2.0 body with a 2024 Orbital Materials copyright, read directly rather than from the license endpoint.

• https://raw.githubusercontent.com/orbital-materials/orb-models/main/MODELS.md recorded 2026-10-13

  Names the training dataset for each released model: OMol25 and OPoly26 for OrbMol, OMat24 or MPA for v3, MPTraj and Alexandria for v2, each with a public link or reference.

• https://raw.githubusercontent.com/orbital-materials/orb-models/main/README.md recorded 2026-10-13

  States that Orb models are licensed under the Apache License, Version 2.0, loads checkpoints from orb_models.forcefield.pretrained with no gate, and documents finetune.py and a finetuning guide but no pretraining entry point.

Adoption is measured from PyPI downloads of orb-models, the declared package that installs the pretrained checkpoints, because PyPI takes precedence over GitHub stars whenever a package is declared.

• https://api.github.com/repos/orbital-materials/orb-models recorded 2026-09-10

  stargazers_count: 616; archived: false; fork: false — recorded so the superseded stars signal stays visible.

• https://pypistats.org/api/packages/orb-models/recent recorded 2026-09-10

  last_month = 12,043 downloads of the orb-models package.

One attribute. Periodic and non-periodic support is coverage rather than a second scale, the direct and conservative variants are separate checkpoints, and the models predict energies, forces and stresses rather than generating structures.

• https://raw.githubusercontent.com/orbital-materials/orb-models/main/README.md recorded 2026-09-10

  Documents finetune.py against any model in ORB_PRETRAINED_MODELS with a dedicated finetuning guide, and describes the models as calculating energies, forces and stresses of atomic systems.